Bridging AI and sociology: a comparative analysis of AI integration in China and the US

Abstract:

Emerging artificial intelligence (AI) technologies have recently gained significant traction beyond industry applications, increasingly permeating academic research across diverse disciplines like computer science and sociology. This widespread adoption marks a transformative shift in scholarly methodologies, particularly enhancing tasks such as extensive data analysis, literature synthesis, and theoretical modeling. The integration of AI into sociological knowledge production represents a fundamental transformation of disciplinary practice. This study examines differential AI adoption patterns across Chinese and American sociology through comparative analysis of three unstructured datasets: institutional seminars and workshops from ten top sociology departments, faculty research profiles, and national sociology conference presentations in recent years. Our findings reveal divergent trajectories as American institutions demonstrated early enthusiasm for AI-related content, while Chinese sociology, traditionally grounded in qualitative approaches, has shown accelerating adoption particularly since 2023. Each country’s national conference topics further reflect these differences. Chinese sociology combines a substantial machine-learning and intelligentisation theme with governance-centered, macro-level narratives of societal transformation, whereas American sociology pairs a dominant cluster on algorithmic systems and large language models with specialized programmes addressing inequality, prediction, and disciplinary reflexivity. These patterns reflect neither simple convergence nor persistent divergence, but rather multiple modernities in academic knowledge production shaped by distinct institutional logics and cultural contexts. The emergence of nation-specific AI tools (e.g., ChatGPT, DeepSeek) may enable distinctive methodological pathways rather than uniform global adoption. This paper highlights the potential of AI to substantially enrich knowledge production in sociology. Understanding the differential patterns of AI adoption within the discipline is essential for developing frameworks that accommodate epistemological pluralism while responsibly leveraging technological capabilities across diverse academic communities.